Methanol Electro-Oxidation on Pt/C and Au @Pt/C Nanoparticles with Different Shapes.
Bibliographic record
Abstract
Nanostructured materials have application on different areas such as catalysis which in part is due to the physical properties gained for the nanometric scale. An important application of nanomaterials is as catalysts in fuel cells, for example, in direct methanol fuel cells (DMFC). Since methanol oxidation kinetics is slow Pt is used as catalyst, Pt is generally employed in nanometric size which improves the electrochemical active area and diminishes the amount of Pt employed, lowering the cost. It has been showed that CO (an intermediary generated during methanol oxidation reaction, MOR) adsorbed and poisons Pt surface [1, 2]. In this regard, many investigations have been focused in improving the catalytic activity of Pt nanoparticles, for example, by synthetizing bimetallic catalysts, alloys, core-shell structures, which allow diminishing the amount of Pt even more and promotes COads oxidation on Pt surfaces [3]. Another fact to take into account is that the surface planes of a nanoparticle are associated to its shape [4, 5], also the extend of an oxidation reaction (including methanol oxidation) depend on the crystallographic planes of the electrocatalyst [6]. In this work, three core-shell catalysts for MOR were prepared by using a colloidal method, core nanoparticles consist on Au NPs and Pt shell, additionally Ag+ was added as modifying agent of shape during synthesis. The amount of Ag+ was used to control the final form of core-shell NPs; all NPs were supported in carbon Vulcan XC-72R and evaluated with electrochemical techniques. Pt NPs with spherical shape were synthesized and electrochemically evaluated for comparison purposes. It was found that the activity of core-shell NPs depends on the amount of Ag+ employed during synthesis; the latter is also associated to the final form of the NP. The synthesized catalysts are listed on Table 1. From electrochemical results, it was found that the activity of catalysts is favored for MOR as follows Au@Pt2/C > Au@Pt3/C> Pt/C> Au@Pt1/C, the current response for MOR is affected by the presence of Au which modifies the surface properties of Pt atoms (shell). Additionally Ag+ concentration leads to preferential planes formation during synthesis, the best current response belongs to Au@Pt2/C, the high current is attributed to the presence of (110) planes, which are the most active for MOR. References Bock, C., B. MacDougall, and C.-L. Sun, Catalysis for Direct Methanol Fuel Cells, in Catalysis for Alternative Energy Generation, L. Guczi and A. Erdôhelyi, Editors. 2012, Springer New York: New York, NY. p. 369-412. Rodríguez, J. and O. Savadogo, Celdas de Combustible de Consumo Directo de Moléculas Orgánicas, in Celdas de combustible, F.J.S.O.H.E. Rodríguez, Editor. 2010: Canada. p. 93-123. Xia, X.H., et al., Structural effects and reactivity in methanol oxidation on polycrystalline and single crystal platinum. Electrochimica Acta, 1996. 41(5): p. 711-718. Niu, W. and G. Xu, Crystallographic control of noble metal nanocrystals. Nano Today, 2011. 6(3): p. 265-285. Yiliguma, Y. Tang, and G. Zheng, Colloidal nanocrystals for electrochemical reduction reactions. Journal of Colloid and Interface Science, 2017. 485: p. 308-327. Xia, Y., et al., Shape-Controlled Synthesis of Metal Nanocrystals: Simple Chemistry Meets Complex Physics? Angewandte Chemie International Edition, 2009. 48(1): p. 60-103. Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".